This paper argues for better concurrency control to support mammoth transactions , which read and write to many items. While these requests are prevalent on graph data, few systems support them efficiently. Currently, developers must make the uncomfortable choice between accepting dismal performance or abandoning transactional semantics. Applications deserve better: we believe that inherent graph properties provide a path forward to designing efficient concurrency control that preserves strong isolation.

Distributed OLTP systems execute the high-overhead, two-phase commit (2PC) protocol at the end of every distributed transaction. Epoch-based commit proposes that 2PC be executed only once for all transactions processed within a time interval called an epoch. Increasing epoch duration allows more transactions to be processed before the common 2PC. It thus reduces 2PC overhead per transaction, increases throughput but also increases average transaction latency. Therefore, required is the ability to choose the right epoch size that offers the desired trade-off between throughput and latency. To this end, we develop two analytical models to estimate throughput and average latency in terms of epoch size taking into account load and failure conditions. Simulations affirm their accuracy and effectiveness. We then present epoch-based multi-commit which, unlike epoch-based commit, seeks to avoid all transactions being aborted when failures occur, and also performs identically when failures do not occur. Our performance study identifies workload factors that make it more effective in preventing transaction aborts and concludes that the analytical models can be equally useful in predicting its performance as well.

The Social Network Benchmark's Business Intelligence workload (SNB BI) is a comprehensive graph OLAP benchmark targeting analytical data systems capable of supporting graph workloads. This paper marks the finalization of almost a decade of research in academia and industry via the Linked Data Benchmark Council (LDBC). SNB BI advances the state-of-the art in synthetic and scalable analytical database benchmarks in many aspects. Its base is a sophisticated data generator, implemented on a scalable distributed infrastructure, that produces a social graph with small-world phenomena, whose value properties follow skewed and correlated distributions and where values correlate with structure. This is a temporal graph where all nodes and edges follow lifespan-based rules with temporal skew enabling realistic and consistent temporal inserts and (recursive) deletes. The query workload exploiting this skew and correlation is based on LDBC's "choke point"-driven design methodology and will entice technical and scientific improvements in future (graph) database systems. SNB BI includes the first adoption of "parameter curation" in an analytical benchmark, a technique that ensures stable runtimes of query variants across different parameter values. Two performance metrics characterize peak single-query performance (power) and sustained concurrent query throughput. To demonstrate the portability of the benchmark, we present experimental results on a relational and a graph DBMS. Note that these do not constitute an official LDBC Benchmark Result - only audited results can use this trademarked term.